Communications of the ACM
Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Computational limitations on learning from examples
Journal of the ACM (JACM)
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Equivalence of models for polynomial learnability
Information and Computation
Learning the Fourier spectrum of probabilistic lists and trees
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Machine Learning
Machine Learning
Results on learnability and the Vapnik-Chervonenkis dimension
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On randomized one-round communication complexity
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
From noise-free to noise-tolerant and from on-line to batch learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On efficient agnostic learning of linear combinations of basis functions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Journal of the ACM (JACM)
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
On the sample complexity of learning functions with bounded variation
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Structural results about exact learning with unspecified attribute values
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning fixed-dimension linear thresholds from fragmented data
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Machine Learning
Sample-efficient strategies for learning in the presence of noise
Journal of the ACM (JACM)
Statistical Learning Theory: A Primer
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces
Theoretical Computer Science
Learning fixed-dimension linear thresholds from fragmented data
Information and Computation
Linear Concepts and Hidden Variables
Machine Learning
Generalization Ability of Folding Networks
IEEE Transactions on Knowledge and Data Engineering
Learning cost-sensitive active classifiers
Artificial Intelligence
Regularization and statistical learning theory for data analysis
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Learnability in Hilbert spaces with reproducing kernels
Journal of Complexity
A Short Review of Statistical Learning Theory
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Learning Preference Relations from Data
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Data-Dependent Margin-Based Generalization Bounds for Classification
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
When Can Two Unsupervised Learners Achieve PAC Separation?
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Learning from examples with unspecified attribute values
Information and Computation
PAC learning of probability distributions over a discrete domain
Theoretical Computer Science
Data-dependent margin-based generalization bounds for classification
The Journal of Machine Learning Research
Efficient algorithms for learning functions with bounded variation
Information and Computation
Function Learning from Interpolation
Combinatorics, Probability and Computing
Fat-Shattering of Affine Functions
Combinatorics, Probability and Computing
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
An incremental decision list learner
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ICML '06 Proceedings of the 23rd international conference on Machine learning
Experience-efficient learning in associative bandit problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Generalized Robust Conjoint Estimation
Marketing Science
Aspects of discrete mathematics and probability in the theory of machine learning
Discrete Applied Mathematics
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
A graphical model for evolutionary optimization
Evolutionary Computation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximal width learning of binary functions
Theoretical Computer Science
Learning nested halfspaces and uphill decision trees
COLT'07 Proceedings of the 20th annual conference on Learning theory
Partial observability and learnability
Artificial Intelligence
Universal ε-approximators for integrals
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Algorithms and theory of computation handbook
Differential privacy and the fat-shattering dimension of linear queries
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
Comparing distributions and shapes using the kernel distance
Proceedings of the twenty-seventh annual symposium on Computational geometry
Data reduction for weighted and outlier-resistant clustering
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Statistical learning approaches with application to face detection
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
Learnability of bipartite ranking functions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Projection-Based PILP: computational learning theory with empirical results
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Distribution-dependent sample complexity of large margin learning
The Journal of Machine Learning Research
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In this paper we investigate a new formal model of machine learning in which the concept (Boolean function) to be learned may exhibit uncertain or probabilistic behavior-thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic concepts (or p-concepts) may arise in situations such as weather prediction, where the measured variables and their accuracy are insufficient to determine the outcome with certainty. We adopt from the Valiant model of learining [28] the demands that learning algorithms be efficient and general in the sense that they perform well for a wide class of p-concepts and for any distribution over the domain. In addition to giving many efficient algorithms for learning natural classes of p-concepts, we study and develop in detail an underlying theory of learning p-concepts.